Multidimensional online report analysis system

Abstract

The invention discloses a multi-dimensional online report analysis system, which comprises a multi-dimensional data model building module, a real-time data processing module, a multi-dimensional report generation module, a deep report analysis module, a data interaction module and a data visualization module. A data model supporting multi-dimensional analysis is constructed, so that the system can adapt to specific analysis requirements of different industries and different enterprises, the applicability and practicability of the system are improved, a multi-dimensional report can be automatically generated through a multi-dimensional report generation module according to dimensions and measurement standards defined by a user, and the user experience is improved. The deep report analysis module provides rich analysis functions such as trend analysis, year-on-year analysis and grouping comparison, and the functions help the user to deeply mine information behind the data and find potential problems and opportunities.

Description

Technical Field

[0001] The present invention belongs to the technical field of report analysis, and specifically refers to a report analysis system based on multi-dimensional online. Background Art

[0002] With the advent of the big data era, enterprise data has exploded. Traditional reporting and analysis systems often have problems such as low data processing efficiency, single dimension, and poor real-time performance, making it difficult to meet enterprises' needs for rapid decision-making in complex business scenarios.

[0003] In addition, the existing multi-dimensional online report analysis still has certain defects. The existing multi-dimensional online report analysis uses predefined data models and cannot be customized according to the user's specific business needs, resulting in low applicability and practicality of the system in different industries and enterprises. It can only generate basic multi-dimensional reports, but lacks in-depth trend analysis, year-on-year analysis, group comparison and other advanced analysis functions, thus limiting the user's ability to mine the information behind the data. Therefore, it is necessary to improve the existing multi-dimensional online report analysis system. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-dimensional online report analysis system to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a report analysis system based on multi-dimensional online, including a multi-dimensional data model building module, a real-time data processing module, a multi-dimensional report generation module, a deep report analysis module, a data interaction module and a data visualization module; The multidimensional data model building module is used to build a data model that supports multidimensional analysis, according to the user's customized data dimensions and metrics according to business needs; The real-time data processing module is used to extract, convert and load data from various data sources and perform real-time data updates; The multi-dimensional report generation module is used to automatically generate a multi-dimensional report based on the dimensions and metrics defined by the user; The in-depth report analysis module is used to deeply analyze the trends and statistics of report data; The data interaction module is used to provide a user interface for data interaction, quickly locate problems and adjust analysis strategies through a real-time feedback mechanism; The data visualization module is used to display data through charts and dashboards.

[0006] Furthermore, the multidimensional data model building module determines the business indicators and classification standards that need to be analyzed according to the needs, and determines the data sources, including databases, files and APIs; selects dimensions according to business needs, each dimension table is provided with a unique natural key, which is used to uniquely identify each record in the dimension, adds a surrogate key to each dimension table, defines the detailed attributes of the dimension and the hierarchical structure within the dimension; and records the detailed information of each transaction in real time; By cleaning the original data, removing duplicate records, filling missing values, correcting errors, converting the data into a consistent format, designing and implementing the ETL process, the fact table is located in the center and multiple dimension tables are connected around it. Based on the star schema, the dimension tables are further normalized to form more fine-grained dimension tables.

[0007] Furthermore, the real-time data processing module identifies and connects to the data source according to requirements, sets the database connection string and API key, extracts files and the extraction time interval according to business requirements, extracts data from various data sources through ETL, and only extracts data that has changed since the last extraction according to the incremental extraction method. The extracted data is checked for integrity and consistency, duplicate records are removed, the data is converted into a unified format, data conversion is performed according to business logic, and the data is pre-aggregated.

[0008] Furthermore, the real-time data processing module loads the cleaned and converted data into the data warehouse according to the data model defined by the multidimensional data model building module, and processes the real-time data stream through the stream processing framework; when processing the data stream, the real-time data stream is divided into small batches for processing, common data is cached in the memory, and indexes are created to speed up queries. In the process of processing the data stream, a monitoring system is set to monitor the status and performance of the ETL process and record detailed logs.

[0009] Furthermore, the multi-dimensional report generation module obtains required data according to the dimensions and metrics selected by the warehouse user based on the data processed by the real-time data processing module, aggregates the data, and sorts the results according to the user's needs, designs the layout of the report according to the dimensions and metrics selected by the user, fills the obtained data into the report, and formats the data.

[0010] Furthermore, the in-depth report analysis module performs analysis based on the data provided by the multi-dimensional report generation module, performs report trend analysis based on the data, and performs analysis based on exponential smoothing, and the implementation formula is: , In the formula, represents the predicted value at time t+1, α represents the smoothing coefficient, represents the actual observed value at time t, It represents the predicted value at time t. When α is close to 1, it depends on the latest observation value, otherwise it depends on the historical observation value. By comparing different time periods, calculating the year-on-year growth of key indicators, and analyzing and comparing by year-on-year growth rate, the implementation formula is: , In the formula, Represents the current period data, Represents data from the same period last year.

[0011] Furthermore, the in-depth report analysis module compares data in different dimensions, calculates the differences between groups, and compares them by the difference in group mean values. The implementation formula is: , In the formula, represents the mean of group A, Represents the mean of group B.

[0012] Furthermore, data prediction is performed based on the data after comparative analysis, a prediction model is set, and the historical data is used for model training. The implementation formula is: , In the formula, represents the observed value at time t, c represents the constant term, represents the autoregressive coefficient, Represents the error term; according to the predicted results, the past error term is used to predict the current error through moving average, and the implementation formula is: , In the formula, represents the moving average coefficient, Represents white noise.

[0013] Furthermore, the data interaction module supports drag-and-drop operations, ad hoc queries, data filtering and sorting functions, and can adjust report content and layout in real time according to set requirements.

[0014] Furthermore, the data visualization module converts the report data into intuitive and easy-to-understand charts, graphs and dashboards, and presents them in a visual manner.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention builds a data model that supports multi-dimensional analysis by allowing users to customize the dimensions and metrics of data according to business needs. This flexibility enables the system to adapt to the specific analysis needs of different industries and enterprises, improving the applicability and practicality of the system. Through the real-time data processing module, the system can extract, convert and load data from various data sources, and perform real-time data updates, ensuring the timeliness and accuracy of the data, so that users can make decisions based on the latest data. 2. The present invention can automatically generate multi-dimensional reports according to the dimensions and metrics defined by the user through the multi-dimensional report generation module, while the in-depth report analysis module provides a wealth of analysis functions, such as trend analysis, year-on-year analysis, group comparison, etc. These functions help users to deeply explore the information behind the data and discover potential problems and opportunities; 3. The present invention provides a user-friendly interface through a data interaction module, supports drag-and-drop operations, ad hoc queries, data filtering and sorting, etc., so that non-professional users can easily explore and analyze data. This interactivity not only improves the user's work efficiency, but also lowers the threshold for data analysis; 4. The present invention displays data through rich charts and dashboards through the data visualization module, allowing users to intuitively understand and analyze data. This visual presentation method helps users quickly capture key information in the data and discover the relationships and trends between the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural schematic diagram of the report analysis system based on multi-dimensional online of the present invention; Figure 2 It is an operation flow chart of a multidimensional data model building module of a multidimensional online report analysis system according to the present invention; Figure 3 The present invention is an operation flow chart of the deep report analysis module based on the multi-dimensional online report analysis system. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-Figure 3 ,The present invention provides a technical solution: including a multidimensional data model building module, a real-time data processing module, a multidimensional report generation module, a deep report analysis module, a data interaction module and a data visualization module; The multidimensional data model building module is used to build a data model that supports multidimensional analysis, according to the user's customized data dimensions and metrics according to business needs; The real-time data processing module is used to extract, convert and load data from various data sources and perform real-time data updates; The multi-dimensional report generation module is used to automatically generate multi-dimensional reports according to the dimensions and metrics defined by the user; The in-depth report analysis module is used to deeply analyze the trends and statistics of report data; The data interaction module is used to provide a user-friendly interface, allowing users to interact with data. Through the real-time feedback mechanism, users can quickly locate problems and adjust analysis strategies; The data visualization module is used to display data through rich charts and dashboards, allowing users to intuitively understand and analyze data.

[0019] Among them, the multidimensional data model construction module builds a data model that supports multidimensional analysis, customizes the dimensions and metrics of data according to business needs, determines the business indicators and classification standards that need to be analyzed according to needs, and determines the data source, including but not limited to databases, files, and APIs; selects dimensions according to business needs, each dimension table has a unique natural key, which is used to uniquely identify each record in the dimension, adds a surrogate key to each dimension table, defines the detailed attributes of the dimension and the hierarchical structure within the dimension; records the detailed information of each transaction in real time; By cleaning the original data, removing duplicate records, filling missing values, correcting errors, converting the data into a consistent format, designing and implementing the ETL process, the fact table is located in the center and multiple dimension tables are connected around it. Based on the star schema, the dimension tables are further normalized to form more fine-grained dimension tables.

[0020] Among them, the real-time data processing module is used to extract, convert and load data from various data sources, and perform real-time data updates, identify data sources and connect to them according to requirements, set necessary connection information such as database connection strings and API keys, and define files for extraction and extraction time intervals according to business requirements. Data is extracted from various data sources through ETL, and only data that has changed since the last extraction is extracted according to the incremental extraction method. The extracted data is checked for integrity and consistency, duplicate records are removed, data is converted into a unified format, data conversion is performed according to business logic, and data is pre-aggregated.

[0021] Among them, the real-time data processing module loads the cleaned and converted data into the data warehouse according to the data model defined by the multidimensional data model building module, processes the real-time data stream through the stream processing framework, divides the real-time data stream into small batches for processing when processing the data stream, caches commonly used data in the memory, and creates appropriate indexes to speed up queries. In the process of processing the data stream, a monitoring system is set to monitor the status and performance of the ETL process and record detailed logs.

[0022] Among them, the multi-dimensional report generation module obtains the required data according to the dimensions and metrics selected by the warehouse user based on the data processed by the real-time data processing module, aggregates the data, and sorts the results according to the user's needs. According to the dimensions and metrics selected by the user, the layout of the report is designed, the data obtained from the treasure house is filled into the report, and the data is formatted.

[0023] The in-depth report analysis module performs analysis based on the data provided by the multi-dimensional report generation module, performs report trend analysis based on the data, and performs analysis based on exponential smoothing. The implementation formula is: , In the formula, represents the predicted value at time t+1, α represents the smoothing coefficient, represents the actual observed value at time t, It represents the predicted value at time t. When α is close to 1, it depends on the latest observation value, otherwise it depends on the historical observation value. By comparing different time periods, calculating the year-on-year growth of key indicators, and analyzing and comparing by year-on-year growth rate, the implementation formula is: , In the formula, Represents the current period data, Represents data from the same period last year.

[0024] The in-depth report analysis module compares data in different dimensions, calculates the differences between groups, and compares them by the difference in group mean values. The implementation formula is: , In the formula, represents the mean of group A, Represents the mean of group B.

[0025] Among them, data prediction is performed based on the data after comparative analysis, a prediction model is set, and the historical data is used for model training. The implementation formula is: , In the formula, represents the observed value at time t, c represents the constant term, represents the autoregressive coefficient, Represents the error term; according to the predicted results, the past error term is used to predict the current error through moving average, and the implementation formula is: , In the formula, represents the moving average coefficient, Represents white noise.

[0026] Among them, the data interaction module is used to provide a user-friendly interface, allowing users to interact with data. Through the real-time feedback mechanism, users can quickly locate problems and adjust analysis strategies. Through the user-friendly interactive interface, non-professional users can also easily explore and analyze data. It supports drag-and-drop operations, ad hoc queries, data filtering and sorting functions, allowing users to adjust report content and layout in real time according to their needs.

[0027] Among them, the data visualization module is used to display data through rich charts and dashboards, allowing users to intuitively understand and analyze data, and convert report data into intuitive and easy-to-understand charts, graphs and dashboards. Through visual presentation, users can more intuitively understand the relationship and trend between data.

[0028] In this example, specifically: the multi-dimensional report generation module obtains the required data according to the dimensions and metrics selected by the warehouse user based on the data processed by the real-time data processing module, aggregates the data, and sorts the results according to the user's needs. According to the dimensions and metrics selected by the user, the layout of the report is designed, the data obtained from the treasure house is filled into the report, and the data is formatted.

[0029] In this example, specifically: the in-depth report analysis module analyzes the data provided by the multi-dimensional report generation module, performs report trend analysis through the data, and performs analysis based on exponential smoothing. The implementation formula is: , In the formula, represents the predicted value at time t+1, α represents the smoothing coefficient, represents the actual observed value at time t, It represents the predicted value at time t. When α is close to 1, it depends on the latest observation value, otherwise it depends on the historical observation value. By comparing different time periods, calculating the year-on-year growth of key indicators, and analyzing and comparing by year-on-year growth rate, the implementation formula is: , In the formula, Represents the current period data, Represents data from the same period last year.

[0030] The in-depth report analysis module compares data in different dimensions, calculates the differences between groups, and compares them by the difference in group mean values. The implementation formula is: , In the formula, represents the mean of group A, Represents the mean of group B.

[0031] Among them, data prediction is performed based on the data after comparative analysis, a prediction model is set, and the historical data is used for model training. The implementation formula is: , In the formula, represents the observed value at time t, c represents the constant term, represents the autoregressive coefficient, Represents the error term; according to the predicted results, the current error is predicted by moving average through the past error term, and the implementation formula is: , In the formula, represents the moving average coefficient, Represents white noise.

[0032] The working principle of the present invention is as follows: According to business needs, users define the dimensions and metrics of data, determine the business indicators, classification standards and data sources that need to be analyzed, determine the business indicators and classification standards, and then design data models based on these indicators and standards, including dimension tables and fact tables. The dimension tables are used to describe the attributes of business data, while the fact tables store specific business measurement values. Data is extracted from various data sources, and data is extracted, converted and loaded through the ETL process. This process includes identifying data sources, setting connection information, defining extraction strategies, data cleaning, data conversion and data loading into data warehouses. The real-time data stream is divided into small batches for processing through the stream processing framework, and memory cache and indexes are used to accelerate queries. At the same time, the monitoring system monitors the status and performance of the ETL process and records detailed logs; The present invention uses a multi-dimensional report generation module to obtain data from the data warehouse according to the dimensions and metrics selected by the user, perform aggregation operations, design the report layout, fill in the data and format it, and finally generate a multi-dimensional report. The deep report analysis module performs deep analysis on the generated report data, including trend analysis, year-on-year analysis, and group comparison. It can also train a prediction model based on historical data to perform data prediction, such as using an autoregressive model combined with a moving average method to perform error prediction. The data visualization module converts report data into visualization forms such as charts, graphs, and dashboards, so that users can intuitively understand and analyze data. The data interaction module provides a user-friendly interface, supports drag-and-drop operations, ad hoc queries, data filtering and sorting, and other functions, so that users can adjust the report content and layout in real time according to their needs. Through the real-time feedback mechanism, users can quickly locate problems and adjust analysis strategies.

[0033] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0034] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A multi-dimensional online report analysis system, characterized by: It includes multi-dimensional data model building module, real-time data processing module, multi-dimensional report generation module, in-depth report analysis module, data interaction module and data visualization module; The multidimensional data model building module is used to build a data model that supports multidimensional analysis, according to the user's customized data dimensions and metrics according to business needs; The real-time data processing module is used to extract, convert and load data from various data sources and perform real-time data updates; The multi-dimensional report generation module is used to automatically generate a multi-dimensional report based on the dimensions and metrics defined by the user; The in-depth report analysis module is used to deeply analyze the trends and statistics of report data; The data interaction module is used to provide a user interface for data interaction, quickly locate problems and adjust analysis strategies through a real-time feedback mechanism; The data visualization module is used to intuitively display data through charts and dashboards.

2. The multi-dimensional online report analysis system according to claim 1 is characterized in that: The multidimensional data model building module determines the business indicators and classification standards to be analyzed according to the needs, and determines the data sources, including databases, files and APIs; selects dimensions according to business needs, and each dimension table is provided with a unique natural key to uniquely identify each record in the dimension, and adds a surrogate key to each dimension table to define the detailed attributes of the dimension and the hierarchical structure within the dimension; and records the detailed information of each transaction in real time; By cleaning the original data, removing duplicate records, filling missing values, correcting errors, converting the data into a consistent format, designing and implementing the ETL process, the fact table is located in the center and multiple dimension tables are connected around it. Based on the star schema, the dimension tables are further normalized to form more fine-grained dimension tables.

3. The multi-dimensional online report analysis system according to claim 1 is characterized in that: The real-time data processing module identifies and connects to the data source according to the requirements, sets the database connection string and API key, extracts files and the extraction time interval according to business requirements, extracts data from various data sources through ETL, and only extracts data that has changed since the last extraction according to the incremental extraction method. The extracted data is checked for integrity and consistency, duplicate records are removed, the data is converted into a unified format, data conversion is performed according to business logic, and the data is pre-aggregated.

4. The multi-dimensional online report analysis system according to claim 3 is characterized in that: The real-time data processing module loads the cleaned and converted data into the data warehouse according to the data model defined by the multidimensional data model building module, and processes the real-time data stream through the stream processing framework; when processing the data stream, the real-time data stream is divided into small batches for processing, common data is cached in the memory, and indexes are created to speed up queries. In the process of processing the data stream, a monitoring system is set to monitor the status and performance of the ETL process and record detailed logs.

5. The multi-dimensional online report analysis system according to claim 1 is characterized in that: The multi-dimensional report generation module obtains the required data according to the dimensions and metrics selected by the warehouse user based on the data processed by the real-time data processing module, aggregates the data, and sorts the results according to the user's needs. It designs the layout of the report based on the dimensions and metrics selected by the user, fills the obtained data into the report, and formats the data.

6. The multi-dimensional online report analysis system according to claim 1, characterized in that: The in-depth report analysis module performs analysis based on the data provided by the multi-dimensional report generation module, performs report trend analysis based on the data, and performs analysis based on exponential smoothing. The implementation formula is: ， In the formula, represents the predicted value at time t+1, α represents the smoothing coefficient, represents the actual observed value at time t, It represents the predicted value at time t. When α is close to 1, it depends on the latest observation value, otherwise it depends on the historical observation value. By comparing different time periods, calculating the year-on-year growth of key indicators, and analyzing and comparing by year-on-year growth rate, the implementation formula is: ， In the formula, Represents the current period data, Represents data from the same period last year.

7. The multi-dimensional online report analysis system according to claim 6 is characterized in that: The in-depth report analysis module compares data in different dimensions, calculates the differences between groups, and compares them by the difference in group mean values. The implementation formula is: ， In the formula, represents the mean of group A, Represents the mean of group B.

8. The multi-dimensional online report analysis system according to claim 7 is characterized in that: According to the data after comparative analysis, data prediction is carried out, a prediction model is set, and the historical data is used for model training. The implementation formula is: ， In the formula, represents the observed value at time t, c represents the constant term, represents the autoregressive coefficient, Represents the error term; according to the predicted results, the past error term is used to predict the current error through moving average, and the implementation formula is: ， In the formula, represents the moving average coefficient, Represents white noise.

9. The multi-dimensional online report analysis system according to claim 1, characterized in that: The data interaction module supports drag-and-drop operations, ad hoc queries, data filtering and sorting functions, and can adjust report content and layout in real time according to set requirements.

10. The multi-dimensional online report analysis system according to claim 1, characterized in that: The data visualization module converts report data into intuitive and easy-to-understand charts, graphs and dashboards, and presents them in a visual manner.

Images